Material Requirements of Decent Living Standards

Decent living standards (DLS) provide a framework to estimate a practical threshold for the energy, GHG, and material consumption required to alleviate poverty. Currently, most research has focused on estimating the energy required to provide the DLS. However, no attempt has been made to estimate the material consumption needed to provide the DLS. Thus, we ask the following questions: First, what is the amount of materials in stocks and flows needed to provide a DLS? Second, which lifestyle and technology choices are effective in providing a DLS without creating an excessive demand for additional materials? To provide a DLS, a material footprint (MF) of 6 t/(cap*yr) with a lower and upper bound between 3 and 14 t/(cap*yr) is required. The direct and indirect in-use stocks required are estimated at 32 t/cap and 11 t/cap, respectively. Nutrition (39%) and mobility (26%) contribute the most to total MF. Buildings account for 98% of direct stocks, while the construction sector accounts for 61% of indirect stocks. We extend the coverage of the DLS by including the collective service dimension and link the material stock-flow-service nexus and life cycle assessment to compute the MF and in-use stocks needed to provide the DLS.

.3 ecoinvent proxy process and their energy content (kcal/gr) for each food category. Table S1. 4 Distribution of disposal waste used. Table S1. 5 Standard array of clothes assumed to be worn, along with weights, washing frequencies, and representative material. Table S1. 6 Mode-shares of pkm between transport modes.   10 Energy content ranges by food category. Table S1. 11 Distribution of alternative waste management scenarios. Table S1. 12 The standard array of clothes is assumed to be worn, along with weights, washing frequencies, and representative material. Table S1. 13 Electricity use in the washing and drying stages, per kg, as a function of an equipment efficiency rating.
Table S1.14 Transport mode shares for the 2DS and B2DS scenarios. Table S1. 15 The type of technologies used in each transport mode was included in the sensitivity analysis. Table S1. 16 List of parameters that are tested in the sensitivity analysis, with their reference values, low values (where applicable), and high values.

Method Description
The procedure to estimate the DLS material footprint is based on four steps. First, we compile a list of services required by each DLS dimension and link them to their respective provisioning systems. Two types of provisioning system are considered: The flow-as-a-service type, such as food consumption for the service of nutrition, and the stock-operation type, where an in-use stock, such as a building, vehicle, IT device, or household appliance, is operated to provide a service to the end users. Taking nutrition as an example, the list contains flows of products such as red meat, chicken, milk, tomatoes, sugar, oil, lentils, and potatoes, energy (electricity and natural gas), and in-use stocks such as refrigerator and microwave (see supplementary information S2 for complete information of the input data used in the study).
In the second step, the reference flows for the different provisioning systems are calculated from the stock-flow-service nexus (i.e., the combination of stocks/flows needed to provide a specific service) 1 . In doing so, we rely on Rao & Min 2 , Millward-Hopkins et al. 3 , and the documentation therein to compile a bundle of services and stocks needed to provide a DLS in different dimensions e.g., nutrition, shelter, transport. The detail for the data sources and the list of provisioning services and reference flows by DLS dimension are provided in supplementary information S1-2, respectively.
In the third and fourth steps, we link the list provisioning systems (services as flows and in-use stocks) with the ecoinvent database to estimate the indirect stocks and material footprints.

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Supplementary information S6-7 presents the indirect stocks, and Supplementary information S3-4 presents the DLS material footprints. See Figure 1 in the paper for a description of the method.
1.1. Process-based life cycle assessment (P-LCA) To estimate the material footprint for providing a DLS we use the approach of Heijungs and Suh 4 , this is rather convenient as the ecoinvent database uses the same computational structure. Thus, Heijungs and Suh first define a "technology matrix" A of flows within the economy that contains an inventory of all recorded flows in the system. Inflows to processes get a negative sign and outflows a positive sign. Additionally, they define the so-called "intervention matrix" B that records resource uptake (input -> negative sign) and emissions (output -> positive sign).
Consequently, The LCA is then formulated as follows: (1) ⋅ = Where A represents the technology matrix, s is a scaling vector and y represents the final demand. Thus, the scaling vector s can be determined by matrix inversion: Similarly, the flows from/to the environment g via: Replacing s in (3) the environmental flows, for example, gangue in ground, iron in ground, can be obtained as follows: Moreover, to translate the environmental flows g for a given vector of demand y into material footprints, g needs to be multiplied for a matrix of characterisation factors C as follows: Replacing (4) in (5) allows us to estimate the material footprint as follows: Where the MF is the material footprint for a given vector of demand. Thus, to estimate the material footprint for providing a decent standard living we use following set of equation for the material footprint in different layers: Here, the hat represents the diagonal of the vector of y or g, respectively. This gives us the MF in two layers, first, (7) the MF for each provisioning system required to satisfy each DLS dimension and (8)  The indirect stocks can be computed from (2) by using the following procedure: First, the resulting vector s (total requirements of goods and services to provide the DLS) is filtered to extract the indirect stocks (stocks different from the in-use stocks for providing the DLS e.g., refrigerator, washing machine, vehicles). In ecoinvent, each unit process is classified by industries according to the international standard classification for industries (ISIC i ), in this case, the ISIC codes 26 to 31 and 40 to 43 that account for capital goods in the manufacturing and construction sector. Thus, we create a new vector to indicate the vector s with all the unit processes with ISIC codes different to capital goods (26 to 31 and 40 to 43) set to zero. Here we also set to Zero all unit processes that account for markets as these do not deliver any particular services.
As the values in are given in flow units i.e., pkm/yr, m²/yr kg/yr. The second step is to convert them into stock units i.e., pkm, m², kg. Thus, we multiply each unit process in by their respective lifetime ( . Where i indicates each unit process in This information is provided by At this point, the stocks are given in units of industrial stocks, for example, unit of building, km of road, and therefore need to be converted into materials i.e., kg of aluminum and cement. Thus, we multiply the by the A matrix to translate into material units i.e., kg of copper, kg of plastic. Once the indirect stocks have been transformed from native units, to material units it is necessary to filter by the target materials chosen in this study. We use the ISIC classification for wood and paper products, plastics, chemicals, basic metals, non-metallic metals, and fabricated metal S9 products (ISIC classification 16,17,20,21,22,23,24,25). We also filter some products that are under the desired classification, but that account for services. For example, injection moulding, extrusion, and rolling services. To do so, we set to zero all products in the vector different from the desired ISIC classification. Supplementary information S6-7 presents the indirect stocks in material units by DLS dimensions and ISIC product types, respectively).

Direct stocks in material units (kg)
The direct stocks are given in units of industrial stocks, e.g., units of phone, laptop, and therefore need to be converted into materials i.e., kg of aluminium, cement. Thus, we multiply the vector by the A matrix to translate into material units i.e., kg of copper, kg of plastic.
We use the same ISIC classification for wood and paper products, plastics, chemicals, basic metals, non-metallic metals, and fabricated metal products (ISIC classification 16,17,20 To match the assumption made by Millward-Hopkins et al. 3 and for simplicity, we assume that urban and rural areas have the same floor space requirements.

Household Buildings and thermal requirements
The building construction archetypes are taken from the RECC database 6 , the RECC model provides information for 52 different types of residential buildings, and 96 non-residential buildings in 20 regions. Data for the building archetypes contain information on the main construction materials (e.g., concrete, steel, wood, paper & cardboard) in kg/m 2 as well as energy consumption for cooling, heating, illumination, and domestic hot water (DHW) in MJ/m 2 based on the different climate zones of the regions. We take from the RECC database the information for the construction materials and the energy consumption for cooling and heating, while for illumination and DHW we perform on our estimations. Additionally, it is assumed that buildings have a lifetime of 80 yrs.
A key innovation of RECC is the upscaling of representative descriptions ('archetypes') with different degrees of material and energy efficiency. The product archetypes were simulated with engineering tools that model building energy balance and vehicle driving cycles 7 .
We take information from the RECC database for a standard single-household family in France as a representation of a temperature zone. we also assume that the heating is provided by gas while cooling is provided with electricity.

Household illumination
We build on the Millward-Hopkins et al. 3 approach and assumptions. To estimate the illumination requirements, it is necessary to acknowledge four key parameters: (i) how much space is illuminated, (ii) for how long each day is illuminated, (iii) how brightly, and (iv) how efficient the process of converting energy into illumination is. Millward-Hopkins et al. 3 Estimations are based on the following equation.
where is the period of illumination (seconds/yr) A is the average floor area illuminated during this time (m 2 ), E is the illuminance (lm/m 2 ) and efficacy gives the efficiency in lm/W. The energy use here is thus in J/yr.
For A we take a value of 15 m 2 /capita and assume that 33% of the space is illuminated. We also take from Millward-Hopkins et al. 3 the assumption that illumination is needed 6 hours/day. Thus = 7.9 million seconds (6 x 60 2 x 365) and illumination minimum level required is 125 lm/m 2 .
Finally, for the efficacy, we take a value of 150 lm/W.
Thus, the energy requirement is ≈ 33 MJ/cap/yr (≈ 7,9 x 15 x 33% x 125/150). We increase this to 36 MJ/cap/yr, to match the value used by Millward-Hopkins et al. 3 . Finally, to consider the capital goods needed to provide the services of illumination, we assume for simplicity and in the absence of more accurate data in ecoinvent that illumination is provided by a compact fluorescent lamp with a lifetime of 1 yr and that one lamp provides the required illumination. We do not assume any energy improvements in illumination given the minor contribution of lighting to total energy use.
This translates into a total raw material input (RMI all materials) of 449.07 kg/(capita*yr) to provide the shelter service. Whereas, for the total material requirements (TMR all), the footprint accounts for 530.03 kg/ yr*capita. S14

Food production.
To estimate the material requirements of food production at all stages of the supply chain 'up to the consumption phase" three parameter needs to be defined: a. An estimate of the global average food requirement (kcal/person/day).
b. The average dietary composition.
c. The material requirements (material footprint) for producing different types of food (kg of a given material footprint/ kg of a given food product).
We follow the same stepwise procedure applied by Millward-Hopkins et al. 3 . First, we obtain the daily calorie requirements per person from the Dietary Guidelines for Americans: 2020-2025 ii , for 30 different age bands (from 2 to 76+ yrs of age). The daily calorie requirements are given by sex (male, female) and activity levels: -Sedentary: A lifestyle that includes only the physical activity of independent living.
-Moderately active: A lifestyle that includes physical activity equivalent to walking about 1.5 to 3 miles per day at 3 to 4 miles per hour, in addition to the activities of independent living.
ii See https://www.dietaryguidelines.gov/resources/2020-2025-dietary-guidelines-online-materials (accessed 08.08.2022) S15 -Active: A lifestyle that includes physical activity equivalent to walking more than 3 miles per day at 3 to 4 miles per hour, in addition to the activities of independent living.
We focus on the central moderately active data as a reference point (see Table S1.1). All data was aggregated in six age groups; then we average across sex according to the proportion of male/female and the proportion of the global population by the respective age band and from the UN World Population Prospects 2022 iii . Here we take the projection for 2023 in a scenario of low fertility as a reference iv , as the growth rate of the global population is slowing.

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To select an appropriate representative product for the different food categories, we set two criteria. First, the product must be commercialized and consumed worldwide, and second, there is a correspondent global market in the ecoinvent 3.8 for the product. With these two criteria, we select the following basket of products: Boneless beef, chicken, cow milk, tomatoes, sugarcane, soybean oil, lentils, and potatoes as a fair representation of red meat, poultry, dairy, fruits & vegetables, sugar, oils, pulses, and staples, respectively, for the references model (see Table S1.3). Transport information is already included in the data, as we use unit process in ecoinvent with global coverage (global geography). Moreover, it is worth mentioning that the values of the eggs in each diet were not considered, since there is no data in ecoinvent to model it.
This translates into a total raw material input (RM all materials) of 1,494 kg/(capita*yr), and a total material requirement (TMR all) value of 2,279 kg/(capita*yr). S18

Cooking and cold storage.
Higher and lower GDP countries use energy differently, with the former typically relying on energy-efficient cooking appliances, while the latter typically relying on inefficient cooking appliances. We follow Millward-Hopkins et al. 3  We assume that cooking is conducted with gas. Similarly, for cold storage, we follow Millward-Hopkins et al. 3 and assume that refrigeration requires 120 KWh/yr.
Finally, to account for the capital goods needed to provide the services of cooking and food storage, we include information for one refrigerator and one stove, with a lifetime of 10 and 15 yrs, respectively, as representation for the cooking appliances.
This translates into a total raw material input (RMI all materials) of 77.55 kg/(capita*yr) and a total material requirement (TMR all) of 88.59 kg/(capita*yr). S19

Water supply
We use the data from Gleick 11 to estimate the water requirements. The authors suggest 50 L/day*capita distributed in four types of home water use: 10 L for cooking, 5 L for drinking, 20 L for sanitation, and 15 L for bathing. This translates into an annual water requirement of 18,250 L/yr*capita. We use the unit process of the market group for tap water (GLO) and the unit process of the market for wastewater, from residence (ROW) to account for the provision and treatment of the water. We assume that the water provided is equal to the water to be treated. Data in ecoinvent for tap water is given in kg, while for the treatment they are provided in m 3 . Here, we assume that 1 kg = 1 L and 1 m 3 = 1,000 L.
For water heating, we assume that only 25% (5 L/day*capita) of the water for sanitation purposes is heated; the remaining 15 liters is for flushing toilets. In addition, we assume that all the water required for bathing is heated.
To estimate the energy required to heat 20 L/day*capita (7,300 L/yr*capita) we use the approach used in Millward-Hopkins et al. 3 . For doing so, we need to define the initial and target temperature of the water (T in and T out ) and heat transfer efficiency by applying the following equation.
where V is the volume of water and ρ the density (0.997 kg/L), C p is the specific heat capacity of the water (4,184 J/kg.K) and Eff is the heat transfer efficiency of the boiler. Taking Eff = 95%, an intensity of 4,390 J/L/K is obtained. This value is multiplied by the total hot water demand (7.300 L/yr*capita) and the temperature parameters. Taking T out = 50°C and an annual global average temperature of 10.7 (France as climatic temperature) as T in we obtain an energy requirement of ≈ 1.3 GJ/yr*capita. Additionally, we assume that natural gas is used for water heating services. Thus, taking a calorific content of 39 MJ/ M 3 for the natural gas, we obtain a total gas requirement of 32.3M 3 /yr*capita.
This results in a total raw material input (RMI all materials) of 50.7 kg/(capita*yr) and a total material requirement (TMR all) of 58.7 kg/(capita*yr).

Waste Management
The starting point to estimate the material footprint for residential waste management is the report of the Word bank What a Waste 2.0 vi . The report provides information about waste production (kg/day*capita) by regions (e.g., North America, Europe, and, Central Asia) and income levels (e.g., low, high) for the6. According to the report, a total of 0.74 kg/day*capita of waste is generated worldwide, while national waste rates fluctuate widely from 0.11 to 4.54 kg/day*capita.
Waste generation volumes are generally correlated with income levels and urbanisation rates. The The second step is to link the information with the appropriate unit process in ecoinvent. represented by their respective processes in ecoinvent. In particular, ecoinvent has information on products that are partly based on recycled products, but it does not have information on the recycling process. Therefore, we assume that the recycling shares in Figure 1.B are assigned to the process of sanitary landfill process. Table S3 summarises the data used to estimate the MF for waste management.
We acknowledge that the values used and provided by ecoinvent for the waste composition of solid waste differ from those reported by the word bank (see Figure S1.1). Particularly, for the food & green which is 13% lower than the values of the Word bank. However, it offers us the opportunity to model the waste disposal in a quite decent manner (see Table S1.4).

Clothing
Clothing requirements correlate with climate variation (e.g., warm countries may require fewer clothes than in cold countries, but may be washed more frequently than in cold countries due to hygiene reasons). However, we assume a standard wardrobe at global level to (i) match the assumptions made by Millward-Hopkins et al. 3 and (ii) simplicity.
Our departure point is the basic clothing list provided by Millward-Hopkins et al. 3 (see Table   S1.5). The data compile information on the weight, average days worn per wash, and main  The second step is to determine the energy consumption per wash and dry. We use data provided by Steinberger et al. 12 . The authors provide information on the energy consumption for washing for different water temperatures and efficiency rates. We used data for 60 °C and the efficiency rate C as a fair representation of the globe. The efficiency rate C was chosen mainly based on the data provided by Gooijer & Stamminger 13 for Europe 0.16 kWh/kg (average estimate for all the countries presented). We also assume that the energy consumed for washing in low-middleincome countries, using less efficient washing machines, is double of the consumed in Europe, 0.33 kWh/kg. We use this value as a representation of the globe, mainly 60% of the countries in the world are low-middle economies vii .
Finally, to estimate the MF we assume that 50% of the cotton clothing (tops, bottoms, underwear) are made of cotton woven and the remaining are made of cotton knit textile. We also include information on the capital goods (wash and dried machine). Both machines have a lifetime of 10 yrs. We use the unit process for the market for washing machines for washing machines and drying machines. i.e., we assume that the production of a dryer machine is similar to the production of a washing machine. Information about water consumption is not considered since it was already included in the hygiene dimension.

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This translates into a total raw material input (RMI all materials) of 123.7 kg/(capita*yr), and a total material requirement (TMR all) of 160.3 kg/ yr*capita.

Education
The estimation of the material footprint for education is based on four factors: (i) the floor-space requirement per student, (ii) the proportion of the population in education age, (iii) the material requirement for the infrastructure, and (iv) the source used to supply the heating service.
For (i) Millward-Hopkins et al. 3 assume that a student requires as much space floor for schooling as for space living (10 m 2 /pupil). Whereas, for (ii) they assume that children from 5-19 age are in school (≈25% of the global population). Finally, for (iii) we use the building construction archetypes from the RECC database 6 as we did in the shelter dimension. For this means, the archetype for non-residential buildings (education) standard for a climate region (France) was taken. Here, we include the energy consumption for DHW from the RECC, as data for water consumption for education is not available. Moreover, we include the illumination service following the same procedure as in the shelter. We assume that the required space is fully illuminated 12 hour/day, since extracurricular activities like sports and working group usually take place at school after regular hours. Finally, from Cullen et al. 14  Different values can be found in the electricity consumption of the literature for the in-use phone.
Grubler et al. 5 provide values in the range of 5-1 watts at full power and standby, respectively.
Moreover, an LCA report for the Fairphone 2 suggests a yearly consumption of 4.9 kWh 16 . We use the higher value provided by Ercan et al. 15 as a reference, mainly because according to Grubler et al. 5 phones replace several electronic functionalities; therefore, an intensive use of phones.

Computers
Rao & Min 2 suggest that one laptop is required per household for a decent living. This translates into a value of 0.25 laptops/per capita. We use the unit process of the market for computers and laptop from ecoinvent to account for it. This data set represents a laptop with a processor speed of 600 MHz, 10 GB RAM, 128 MB memory, a 12.1 inch screen, and a total mass with an expansion base of 3.15 kg with a lifetime of 4.25 yrs 9 .
Regarding electricity consumption. We take the value of (62 kWh/yr) provided by Deng et al. 17 .

Networks and Infrastructure
We include the unit process market for routers, internet as a representation of the capital goods needed to provide and support telecommunications. This unit process represents a domestic router with a maximum data rate of 100 Mbit/s and a realistic data rate of 25 Mbit/s and a useful life of 6 yrs 9 . We assume one router/household or 0.25/capita.
Regarding electricity consumption, we use the data provided by Ercan et al. 15 5 suggest a minimum of 7,000 km/capita*yr for mobility based on the results obtained for Japan, an affluent society with an advanced transportation system. Alternatively, the two-degree scenario (2DS) suggests average annual mobility levels in 2025 ranging from <5,000 S30 km/cap in India to >20,000 km/cap in the USA. While, the world average annual mobility in the world will increase from ~7,000 km/cap in 2014 to ~10,000 km/cap by 2060 18 .
We take these values as a reference and follow the Millward-Hopkins et al. 3  Where MOB is the annual mobility requirements per capita (pkm/(yr*capita)), (f fixed ) represents a proportion of mobility that is independent (travel that is independent of local population density), (f variable ) is inversely proportional to population density, and (LD) is the lived density.
To estimate LD we use the global density information for 2010 provided by the World Bank Database xi . This gives us a value of 138.6 (53 capita/ km 2 *1.32E+08 km 2 / (4.7E+07 km 2 + 3.6E+06 km 2 )). This gives a total mobility requirement of 8,274 km/capita*yr.

Assumptions on the Modal Split
The next step is to specify the share of transport modes. We divided transport modes into five categories (non-motorised transport, air transport, rail transport, road transport buses, road transport-cars, and road transport-2 wheelers). For non-motorised transport (e.g., walking or biking), we use the value provided by Millward-Hopkins et al. (2020) (4 km/capita*day or 1,460 km/capita*yr). For the rest of the transport modes, we use the values of the 2060 reference technology scenario (RTS) provided by IEA 18 (see Table S1.6). The last step is to select the appropriate unit process to model each transport mode. Ecoinvent has a wide range of processes to this end. For example, for the air mode, ecoinvent has four options depending on the distance of the haul (very short, sort, medium and long). Similarly, there are different options for road transport modes (except for the train that only has the diesel option), depending on the fuel and the size of the vehicle. Thus, we made the following assumptions for S32 the reference scenario: (i) the 1,460 pkm associated with non-motorised vehicles is provided by a bike, (ii) for the 2-wheelers, the information for a motor scooter(petrol) is used, (iii) for buses and train, we use the information for a regular bus and a long-distance train operated with diesel, (iv) for the car, we take a medium-size car impulse by natural gas, and (v) for the aircraft, we use the information for a medium haul.
It is worth mentioning that in ecoinvent the unit reference for all transport modes different from the car is pkm, whereas for the car the unit reference is given in km. Therefore, for simplicity, we assume an occupancy rate of 1 person per car to account for the pkm by car assumed in each scenario.
This translates into a total raw material input (RMI all materials) of 1,346.28 kg/(capita*yr) and a total material requirement (TMR all) of 1,555.74 kg/capita*yr.

Collective services
We include information on additional public-government infrastructures required to support modern societies. The rationale behind this is that to promote and support a modern society different non-residential building such as wholesale & retail, offices, hotels & restaurants, and sports facilities are needed.
To account for the demand for non-residential buildings per capita, we start with the information provided by the BPIE 19 for Europe. The report compiles information for different types of S33 building across 30 Europe countries including the population of each country. In short, the report concludes that 25% of the total buildings in Europe are non-residential buildings used to support different activities (see Table S1.7). Here, we made two assumptions. First, we exclude the values for education and hospitals, as we already include them in their respective DLS dimension.
Second, for the remaining building archetypes we take half of the non-residential requirements (4.69 = 9.40/2) as a proxy to the minimum requirements of collective infrastructures required to provide social wellbeing. Total non-residential building 9.40 We take from the RECC database the archetypes associated with each type of building for a climate temperature (France). Moreover, we also include the illumination service with the following assumption: First, the required space is fully illuminated for 12 hour/day with an illuminance level of 430 lm/m 2 for offices as a reference value.
In this section, we describe the parameters and values chosen for the DLS dimension sensitivity analysis.

Shelter
As the type of temperature zone and the building determine the demand for materials and energy in buildings, we perform a sensitivity analysis with the remaining 34 building archetypes of the RECC model. Moreover, we include information on India as a representation of a warmer zone as a proxy for Asia, where about 2/3 of the world's population lives. Finally, we also assume that all services demanded by the household, i.e., cooling, heating, and illumination, are provided by electricity.
Additionally, for illumination, we assume that the service uses twice as many lights that are used twice the time. Similar assumptions are used in Millward-Hopkins et al. 3

Nutrition
Different parameters influence the MF of nutrition. (i) lifestyle, (ii) the type of diet, and (iii) type of food consumed to provide the required kcal/day. For (i), we include the information on a sedentary and active lifestyle from the Dietary Guidelines for Americans: 2020-2025 i (see Table   S1.8). All data were aggregated into six ages, then we averaged across sex according to the proportion of male/female and the proportion of the total population by the respective age band from the UN World Population Prospects 2022 ii .  For (iii) as the energy content (kcal/gr) of food dramatically varies across products and categories (e.g., for red meat: 0.77 kcal per gr of buffalo meat and 1.5 kcal per gr of Beef Boneless), the product chosen as a proxy for each food category may significantly influence the final results.
Thus, we estimate the average, maximum and minimum energy content (kcal/gr) of each food category based on the FAO's food balance sheets xii (see table S1.10). Here, it is worth mentioning that, while we change the energy content (kcal/gr) of the different food products, we do not change the unit process from the ecoinvent used to estimate the material footprints of the different diets. Additionally, as the products to satisfy a specific diet vary across cultures, e.g., rice is more consumed than potatoes in India than in Europe, we replace the potatoes for rice and wheat to provide a range of variation (including the ecoinvent unit process). Finally, we also include a microwave as a cooking appliance.
We acknowledge that this pragmatic approach may lack precision, since the supply chain of the different products may differ considerably (e.g., bananas and tomatoes). However, since we intend to provide a range of values in which the different types of diets may fall, more than provide an optimised basket of products to satisfy a determined diet, we consider this model approach a fair representation of the wide spectrum of possible results.
Additionally, to consider the energy sources and efficiency of the appliance needed to provide the service of nutrition, we prepare additional scenarios. We assume 25% or 75% of the kcal xiii See https://www.fao.org/3/X9892E/X9892e05.htm#P8217_125315 (accessed 08.08.2022) consumed is cooked, moreover, we assume cooking is conducted with electricity. According to Cullen et al. 14 , the energy intensity of cooking and refrigeration could be reduced by 80%. Thus, we also consider an efficiency scenario in which services are 80% more efficient (i.e., 0.8 KJ/kcal of food for cooking and 24 KWh/yr for refrigeration).
In total, we conducted 3,456 scenarios for the nutrition dimension. High values are obtained for HGD active lifestyle with HGD in which rice is used to approach the staple. This scenario also assumes that a large number of kcal is cooked with electricity in standard devices (refrigeration and stove), and the kcal/gr of the selected products is the minimum of the available products from the FAO. This scenario gives a total raw material input (RMI) of 3,534.9 kg/(capita*yr), whereas for TRM the value is 5,213.1 kg/(capita*yr). On the other hand, the lowest values are obtained for a sedentary lifestyle, in which potatoes are chosen as a representation of the staple. Moreover, a low number of kcal is cooked with natural gas. This scenario also assumes that all the devices (microwave as cooking appliance) used are efficient and the kcal/gr of the selected products is the maximum of the available products from the FAO. Concretely, the result for RMI is 215.4 kg/(capita*yr), whereas, for TMR and TWF result is 315.6 kg/(capita*yr).

Hygiene.
The energy consumption for water heating depends mainly on three factors, the initial and target water temperature, the amount of water to be heated, and the efficiency assumed. For the former,

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we used a yearly average temperature of India of 23.65 ° C as a representation of a warm land as the initial temperature and a value of 60 ° C of the target temperature. Below 65 ° C, a typical value according to Cullen et al. 14 . For the amount of water to be heated, we increase the value by a factor of 2 (14,600 lt/yr*capita) to match the recommendation of the ONU xiv (50-100 lt/yr*capita). Finally, for the latter, we assume an efficiency for the heating water of 98%.
Regarding waste management, there are two key parameters: (i) the amount of waste produced and (ii) the disposition technology. We use the former two values for waste production, the maximum (4.54 kg/capita/day) and minimum (0.11 kg/capita/day) values from Word bank What a Waste 2.0. For the latter, we include two future alternatives in which we increase 50% the amount of waste to be composted and incinerated, respectively, in each scenario. By doing so, we assume that the other disposition technologies different from incineration and composting decrease in equal proportions to maintain unity (see table S1.11)

Mobility
The impacts associated with mobility are mainly determined by three factors (the amount of km/capita*yr required, the share of each transport mode to satisfy the desired pkm requirements, and the type of technology of each transport mode. We decide to modify the two latter parameters.

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First, we use the 2 ° C scenario (2DS) and the Beyond 2 ° C scenario (B2DS) provided by the IEA 18 (see table S1.14) to determine the shares of transport modes. Second, for the transport modes we develop several scenarios, we assume that the shares of mobility provided by non-motorized vehicles can be provided by walking (in which no impacts are associated), biking (regular or E-bike), and a combination of 50-50 between walking and biking (regular or E-bike), similarly, for the 2-wheelers transport shares we include an E-scooter.
For the passenger light-duty vehicles (PLDVs), four types of vehicles are included, two transport modes for the buses, and four for aircraft are also included. See Table S1.15.
Include not using dryer machine to account for warm regions